Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.

Recently, ChatGPT, an advanced AI language model, has transformed the tourism industry by redefining how travelers access information and experience destinations. This survey explores the multifaceted potential of ChatGPT in various sectors of the tourism domain. The study begins by addressing the ethical considerations related to AI and natural language processing, emphasizing the necessity of privacy and data security in ChatGPT interactions. It analyzes the recent research findings, demonstrating ChatGPT’s efficacy in translating languages for tourists and improving services. The technology’s impact on tourism education and research is explored, highlighting its disruptive effects, benefits, and challenges, particularly in academic contexts. The paper delves into ChatGPT’s influence on the hospitality and tourism sector, focusing on its role in customer interactions, backend operations, and research methodologies. Additionally, it examines ChatGPT’s implications for content creation, visitor motivations, cultural perceptions, and regional tourism management. These insights shed light on ChatGPT’s potential to enhance customer experiences, influence visitor behaviors, and bridge cultural gaps in diverse tourism contexts. Concluding with preliminary guidelines for ChatGPT adoption, this paper equips industry professionals with essential knowledge to leverage the technology effectively. By embracing ChatGPT, the tourism industry can provide travelers with more informed, personalized, and immersive experiences, thus enhancing overall satisfaction and engagement during their journeys.

In contrast to earlier ABSA studies primarily concentrating on individual sentiment components, recent research has ventured into more complex ABSA tasks encompassing multiple elements, including pair, triplet, and quadruple sentiment analysis. Quadruple sentiment analysis, also called aspect-category-opinion-sentiment quadruple Extraction (ACOSQE), aims to dissect aspect terms, aspect categories, opinion terms, and sentiment polarities while considering implicit sentiment within sentences. Nonetheless, a comprehensive overview of ACOSQE and its corresponding solutions is currently lacking. This is the precise gap that our survey seeks to address. To be more precise, we systematically reclassify all subtasks of ABSA, reorganizing existing research from the perspective of the involved sentiment elements, with a primary focus on the latest advancements in the ACOSQE task. Regarding solutions, our survey offers a comprehensive summary of the state-of-the-art utilization of language models within the ACOSQE task. Additionally, we explore the application of ChatGPT in sentiment analysis. Finally, we review emerging trends and discuss the challenges, providing insights into potential future directions for ACOSQE within the broader context of ABSA.

Numerous studies have delved into the applications of ChatGPT across various domains such as medicine, sports, education, and business analysis. ChatGPT emerges as a potential replacement for key contributors in these diverse fields, sparking an ongoing quest to validate this assertion. One focal point of this paper is the examination of GPT-4's, the fourth generation of Chat GPT, capacity to handle a spectrum of visual elements like images, pictures, flowcharts, plots, and diagrams. The inquiry extends to assessing how the gleaned information from these visuals compares with human intuition, both inductive and deductive. To investigate, GPT-4 was presented with samples of human faces, flowcharts, plots, and diagrams, leading to remarkably accurate and error-free results within the specified timeframe, surpassing human capabilities. The outcomes underscore GPT-4's impressive prowess in image analysis, covering identification, recognition, and contextual understanding of visual content. Furthermore, GPT-4's proficiency in identifying objects within individual images opens the door to be utilized comprehensively in the field of object detection. However, GPT-4 exhibits limitations in recognizing individual images due to privacy considerations.